Peter J. Vickery

Bio: Peter J. Vickery is an academic researcher from University of Western Ontario. The author has contributed to research in topics: Wind speed & Storm. The author has an hindex of 17, co-authored 38 publications receiving 3721 citations.

Reduced drag coefficient for high wind speeds in tropical cyclones

Abstract: The transfer of momentum between the atmosphere and the ocean is described in terms of the variation of wind speed with height and a drag coefficient that increases with sea surface roughness and wind speed. But direct measurements have only been available for weak winds; momentum transfer under extreme wind conditions has therefore been extrapolated from these field measurements. Global Positioning System sondes have been used since 1997 to measure the profiles of the strong winds in the marine boundary layer associated with tropical cyclones. Here we present an analysis of these data, which show a logarithmic increase in mean wind speed with height in the lowest 200 m, maximum wind speed at 500 m and a gradual weakening up to a height of 3 km. By determining surface stress, roughness length and neutral stability drag coefficient, we find that surface momentum flux levels off as the wind speeds increase above hurricane force. This behaviour is contrary to surface flux parameterizations that are currently used in a variety of modelling applications, including hurricane risk assessment and prediction of storm motion, intensity, waves and storm surges.

Simulation of hurricane risk in the u.s. using empirical track model

Abstract: This paper describes a new technique for modeling hurricane risk in the United States. A storm track modeling approach is employed where, for each hurricane, the entire track as it crosses the ocean and makes landfall is modeled. The central pressure is modeled as a function of the sea surface temperature. The approach is validated through comparisons of simulated and observed key hurricane statistics (central pressure, translation speed, heading, and approach distance) along the U.S. coastline. The simulated and observed landfall rates of intense hurricanes (Saffir-Simpson Scale 3 and higher) also are compared on a regional basis along the coast. The model is able to reproduce the continuously varying hurricane climatology along the U.S. coastline, and it provides a rational means for examining the hurricane risk for geographically distributed systems such as transmission lines and insurance portfolios.

Hurricane wind field model for use in hurricane simulations

Abstract: A critical component in the simulation of hurricanes is a good representation of the hurricane wind field when given information of the storm intensity, size, and translation speed. In the investigation described here, the full nonlinear solution to the equations of motion of a hurricane are solved and then parameterized for use in fast-running simulations. The hurricane model described here takes into account the effects of changing sea surface roughness and the air-sea temperature difference on the estimated surface-level wind speeds. Comparisons between modeled and observed hurricane wind speed records are performed where one compares both the 10-min mean wind speeds and the peak gust wind speeds. The resulting wind field model represents the most physically based and validated model used in the estimate of hurricane wind speed exceedance probabilities.

HAZUS-MH Hurricane Model Methodology. II: Damage and Loss Estimation

Abstract: An overview of the damage and loss models used in the HAZUS-MH Hurricane Model is presented. These models represent the last two of five major component models used in HAZUS for the prediction of damage and loss to buildings subjected to hurricanes. The damage and loss models have been validated using damage data collected during poststorm damage surveys and insurance loss data. The HAZUS Hurricane Model represents an advance in the state of the art over most hurricane loss prediction models, in that it estimates wind induced loads, building response, damage, and then loss, rather than simply using historical loss data to model loss as a function of wind speed. A mitigation example is presented that shows the expected reductions in losses achieved by strengthening the roof of a building and adding window protection.

A Hurricane Boundary Layer and Wind Field Model for Use in Engineering Applications

Abstract: This article examinesthe radial dependence of the height of the maximum wind speed in a hurricane, which is found to lower with increasing inertial stability (which in turn depends on increasing wind speed and decreasing radius) near the eyewall. The leveling off, or limiting value, of the marine drag coefficient in high winds is also examined. The drag coefficient, given similar wind speeds, is smaller for smaller-radii storms; enhanced sea sprayby short or breaking waves is speculatedas a cause. Afitting technique of dropsonde wind profiles is used to model the shape of the vertical profile of mean horizontal wind speeds in the hurricane boundary layer, using only the magnitude and radius of the ‘‘gradient’’ wind. The method slightly underestimates the surface winds in small but intense storms, but errors are less than 5% near the surface. The fit is then applied to a slab layer hurricane wind field model, and combined with a boundary layer transition model to estimate surface winds over both marine and land surfaces.

Increasing destructiveness of tropical cyclones over the past 30 years

Abstract: Theory and modelling predict that hurricane intensity should increase with increasing global mean temperatures, but work on the detection of trends in hurricane activity has focused mostly on their frequency and shows no trend. Here I define an index of the potential destructiveness of hurricanes based on the total dissipation of power, integrated over the lifetime of the cyclone, and show that this index has increased markedly since the mid-1970s. This trend is due to both longer storm lifetimes and greater storm intensities. I find that the record of net hurricane power dissipation is highly correlated with tropical sea surface temperature, reflecting well-documented climate signals, including multi-decadal oscillations in the North Atlantic and North Pacific, and global warming. My results suggest that future warming may lead to an upward trend in tropical cyclone destructive potential, and--taking into account an increasing coastal population--a substantial increase in hurricane-related losses in the twenty-first century.

Changes in tropical cyclone number, duration, and intensity in a warming environment.

Abstract: We examined the number of tropical cyclones and cyclone days as well as tropical cyclone intensity over the past 35 years, in an environment of increasing sea surface temperature. A large increase was seen in the number and proportion of hurricanes reaching categories 4 and 5. The largest increase occurred in the North Pacific, Indian, and Southwest Pacific Oceans, and the smallest percentage increase occurred in the North Atlantic Ocean. These increases have taken place while the number of cyclones and cyclone days has decreased in all basins except the North Atlantic during the past decade.

The global climatology of an interannually varying air–sea flux data set

Abstract: The air–sea fluxes of momentum, heat, freshwater and their components have been computed globally from 1948 at frequencies ranging from 6-hourly to monthly. All fluxes are computed over the 23 years from 1984 to 2006, but radiation prior to 1984 and precipitation before 1979 are given only as climatological mean annual cycles. The input data are based on NCEP reanalysis only for the near surface vector wind, temperature, specific humidity and density, and on a variety of satellite based radiation, sea surface temperature, sea-ice concentration and precipitation products. Some of these data are adjusted to agree in the mean with a variety of more reliable satellite and in situ measurements, that themselves are either too short a duration, or too regional in coverage. The major adjustments are a general increase in wind speed, decrease in humidity and reduction in tropical solar radiation. The climatological global mean air–sea heat and freshwater fluxes (1984–2006) then become 2 W/m2 and −0.1 mg/m2 per second, respectively, down from 30 W/m2 and 3.4 mg/m2 per second for the unaltered data. However, decadal means vary from 7.3 W/m2 (1977–1986) to −0.3 W/m2 (1997–2006). The spatial distributions of climatological fluxes display all the expected features. A comparison of zonally averaged wind stress components across ocean sub-basins reveals large differences between available products due both to winds and to the stress calculation. Regional comparisons of the heat and freshwater fluxes reveal an alarming range among alternatives; typically 40 W/m2 and 10 mg/m2 per second, respectively. The implied ocean heat transports are within the uncertainty of estimates from ocean observations in both the Atlantic and Indo-Pacific basins. They show about 2.4 PW of tropical heating, of which 80% is transported to the north, mostly in the Atlantic. There is similar good agreement in freshwater transport at many latitudes in both basins, but neither in the South Atlantic, nor at 35°N.

Reduced drag coefficient for high wind speeds in tropical cyclones

Abstract: The transfer of momentum between the atmosphere and the ocean is described in terms of the variation of wind speed with height and a drag coefficient that increases with sea surface roughness and wind speed. But direct measurements have only been available for weak winds; momentum transfer under extreme wind conditions has therefore been extrapolated from these field measurements. Global Positioning System sondes have been used since 1997 to measure the profiles of the strong winds in the marine boundary layer associated with tropical cyclones. Here we present an analysis of these data, which show a logarithmic increase in mean wind speed with height in the lowest 200 m, maximum wind speed at 500 m and a gradual weakening up to a height of 3 km. By determining surface stress, roughness length and neutral stability drag coefficient, we find that surface momentum flux levels off as the wind speeds increase above hurricane force. This behaviour is contrary to surface flux parameterizations that are currently used in a variety of modelling applications, including hurricane risk assessment and prediction of storm motion, intensity, waves and storm surges.

Reliability Engineering and System Safety

Abstract: This paper presents a polynomial dimensional decomposition (PDD) method for global sensitivity analysis of stochastic systems subject to independent random input following arbitrary probability distributions. The method involves Fourier-polynomial expansions of lower-variate component functions of a stochastic response by measure-consistent orthonormal polynomial bases, analytical formulae for calculating the global sensitivity indices in terms of the expansion coefficients, and dimension-reduction integration for estimating the expansion coefficients. Due to identical dimensional structures of PDD and analysis-of-variance decomposition, the proposed method facilitates simple and direct calculation of the global sensitivity indices. Numerical results of the global sensitivity indices computed for smooth systems reveal significantly higher convergence rates of the PDD approximation than those from existing methods, including polynomial chaos expansion, random balance design, state-dependent parameter, improved Sobol’s method, and sampling-based methods. However, for non-smooth functions, the convergence properties of the PDD solution deteriorate to a great extent, warranting further improvements. The computational complexity of the PDD method is polynomial, as opposed to exponential, thereby alleviating the curse of dimensionality to some extent. Mathematical modeling of complex systems often requires sensitivity analysis to determine how an output variable of interest is influenced by individual or subsets of input variables. A traditional local sensitivity analysis entails gradients or derivatives, often invoked in design optimization, describing changes in the model response due to the local variation of input. Depending on the model output, obtaining gradients or derivatives, if they exist, can be simple or difficult. In contrast, a global sensitivity analysis (GSA), increasingly becoming mainstream, characterizes how the global variation of input, due to its uncertainty, impacts the overall uncertain behavior of the model. In other words, GSA constitutes the study of how the output uncertainty from a mathematical model is divvied up, qualitatively or quantitatively, to distinct sources of input variation in the model [1].